Image analysis enhanced related item decision

ABSTRACT

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining from a user one or more data queries; identifying a product of interest in response to the one or more data query; examining a plurality of product records to determine a set of related products that are related to the product of interest, wherein the examining includes performing image analysis to extract one or more product topic classifier from product image data representing one or more product; and providing one or more output in response to the examining.

BACKGROUND

Image analysis involves the extraction of meaningful information fromimages such as digital images and can include various digital imageprocessing processes. Digital image processing processes can includeprocesses for noise removal, edge sharpening, object recognition andimage segmentation.

Data structures have been employed for improving operation of computersystem. A data structure refers to an organization of data in a computerenvironment for improved computer system operation. Data structure typesinclude containers, lists, stacks, queues, tables and graphs. Datastructures have been employed for improved computer system operatione.g. in terms of algorithm efficiency, memory usage efficiency,maintainability, and reliability.

Artificial intelligence (AI) refers to intelligence exhibited bymachines. Artificial intelligence (AI) research includes search andmathematical optimization, neural networks and probability. Artificialintelligence (AI) solutions involve features derived from research in avariety of different science and technology disciplines ranging fromcomputer science, mathematics, psychology, linguistics, statistics, andneuroscience.

SUMMARY

Shortcomings of the prior art are overcome, and additional advantagesare provided, through the provision, in one aspect, of a method. Themethod can include, for example: obtaining from a user one or more dataqueries; identifying a product of interest in response to the one ormore data query; examining a plurality of product records to determine aset of related products that are related to the product of interest,wherein the examining includes performing image analysis to extract oneor more product topic classifier from product image data representingone or more product; and providing one or more output in response to theexamining.

In another aspect, a computer program product can be provided. Thecomputer program product can include a computer readable storage mediumreadable by one or more processing circuit and storing instructions forexecution by one or more processor for performing a method. The methodcan include, for example: obtaining from a user one or more dataqueries; identifying a product of interest in response to the one ormore data query; examining a plurality of product records to determine aset of related products that are related to the product of interest,wherein the examining includes performing image analysis to extract oneor more product topic classifier from product image data representingone or more product; and providing one or more output in response to theexamining.

In a further aspect, a system can be provided. The system can include,for example a memory. In addition, the system can include one or moreprocessor in communication with the memory. Further, the system caninclude program instructions executable by the one or more processor viathe memory to perform a method. The method can include, for example:obtaining from a user one or more data queries; identifying a product ofinterest in response to the one or more data query; examining aplurality of product records to determine a set of related products thatare related to the product of interest, wherein the examining includesperforming image analysis to extract one or more product topicclassifier from product image data representing one or more product; andproviding one or more output in response to the examining.

Additional features are realized through the techniques set forthherein. Other embodiments and aspects, including but not limited tomethods, computer program product and system, are described in detailherein and are considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects of the present invention are particularly pointedout and distinctly claimed as examples in the claims at the conclusionof the specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts a system having a manager system, client computerdevices, and an administrator client computer device according to oneembodiment;

FIG. 2 is a flowchart illustrating a method for performance by a managersystem according to one embodiment;

FIG. 3 is a flowchart illustrating a method for performance by a managersystem according to one embodiment;

FIG. 4 is a flowchart illustrating a method for performance by a managersystem interoperating with other components according to one embodiment;

FIG. 5 depicts an administrator user interface according to oneembodiment;

FIG. 6 depicts a user interface facilitating searching for a product ofinterest by a user according to one embodiment;

FIG. 7 is a flowchart illustrating a method for performance by a managersystem according to one embodiment;

FIG. 8 depicts content that can be returned by subjecting a product ofinterest image to image analysis according to one embodiment;

FIG. 9 depicts content that can be returned by subjecting a candidaterelated product image to image analysis according to one embodiment;

FIG. 10 depicts a computing node according to one embodiment;

FIG. 11 depicts a cloud computing environment according to oneembodiment; and

FIG. 12 depicts abstraction model layers according to one embodiment.

DETAILED DESCRIPTION

System 100 for use in supporting the generation of a recommendation isshown in FIG. 1. System 100 can include manager system 110 having anassociated data repository 112, administrator client computer device125, a plurality of client computer devices 130A-130Z, and social mediasystem 140. Manager system 110, administrator client computer device125, a plurality of client computer devices 130A-130Z, and social mediasystem 140 can be in communication with one another via network 180.System 100 includes numerous devices, which may be computing node baseddevices, connected by a network 180. Network 180 may be a physicalnetwork and/or a virtual network. A physical network can be, forexample, a physical telecommunications network connecting numerouscomputer nodes or systems, such as computer servers and computerclients. A virtual network can, for example, combine numerous physicalnetworks or parts thereof into a logical virtual network. In anotherexample, numerous virtual networks can be defined over a single physicalnetwork.

In one embodiment manager system 110 can be external to client computerdevice 130A-130Z administrator client computer device 125 and socialmedia system 140. In one embodiment manager system 110 can be co-locatedwith one or more or client computer device 130A-130Z administratorclient computer device 125 and social media system 140.

Each of the different client computer devices 130A-130Z can beassociated to a different user. Regarding one or more client computerdevice 130A-130Z, a computer device of one or more client computerdevice 130A-130Z in one embodiment can be a computing node deviceprovided by a client computer, e.g. a mobile device, e.g. a smartphoneor tablet, a laptop, smartwatch or PC that runs one or more program,e.g. including a web browser for opening and viewing web pages.

Social media system 140 can include a collection of files, including forexample, HTML files, CSS files, image files, and JavaScript files.Social media system 140 can be a social website such as FACEBOOK®(Facebook is a registered trademark of Facebook, Inc.), TWITTER®(Twitter is a registered trademark of Twitter, Inc.), LINKEDIN®(LinkedIn is a registered trademark of LinkedIn Corporation), orINSTAGRAM® (Instagram is a registered trademark of Instagram, LLC).Computer implemented social networks incorporate messaging systems thatare capable of receiving and transmitting messages to client computersof participant users of the messaging systems. Messaging systems canalso be incorporated in systems that that have minimal or no socialnetwork attributes. A messaging system can be provided by a shortmessage system (SMS) text message delivery service of a mobile phonecellular network provider, or an email delivery system. Manager system110 can include a messaging system in one embodiment.

Manager system 110 can run various processes including preparation andmaintenance process 111, Natural Language Processing (NLP) process 113,product registering process 114, product examining process 115, imageanalysis process 116, and machine learning process 117.

Data repository 112 can store various data, such as product records datafor promotion of products and decision data structures for use infacilitating decisions by artificial intelligence (AI).

Manager system 110 running product registering process 114 canfacilitate the registration of product records into data repository 112.When a new product is registered and can be made available foridentification in response to one or more search query by a user whouses system 100 to identify products of interest. Manager system 110running product registering process 114 can make available anadministrator user interface for registration of products and anadministrator user can use the administrator user interface to registernew products. Such administrator user interface can include various datafields, e.g. allowing an administrator user to specify such informationas a product name, product brand, product identifier, e.g. serial ID, aproduct image, product keywords, product specifications, productdescription, and/or a product sales price.

Manager system 110 running product examining process 115 can use imageanalysis for determining a set of related products that are related to aproduct of interest. Manager system 110 running product examiningprocess 115 can run image analysis process 116. Manager system 110running product examining process 115 can subject one or more image ofone or more product to image analysis to extract topic classificationsfor one or more product image. Manager system 110 can use the extractedtopic classification information as an input to a determination of arelated set of products related to a product of interest. Manager system110 running product examining process 115 can subject different types ofproduct images to an image analysis for extraction of topicclassifications. Manager system 110 running product examining process115 can subject an image of a product of interest to image analysis forextraction of one or more topic classification. Manager system 110running product examining process 115 alternatively or in addition to,can subject an image of a candidate related product for a product ofinterest to an image analysis for extraction of one or more topicclassification of a candidate related product.

Manager system 110 running image analysis process 116 can subjectproduct image data to image processing to return product topicclassifiers with associated confidence levels and markup languagecontent that specifies the topic classifiers and confidence levels.

Manager system 110, running machine learning process 117 can update oneor more process run by manager system 110 based on obtained data toimprove accuracy and/or reliability of the one or more process. In oneembodiment, manager system 110 running product examining process 115 canuse a decision data structure that maps product dissimilarity scores todecisions. Such a decision data structure can have parameter values,such as parameter values specifying decision thresholds and can be independence on a function having function parameter values such as ascoring parameter values for scoring product dissimilarities. Managersystem 110 in one embodiment can run a plurality of instances of such adecision data structure, e.g. each instance for a different web-basedsession facilitating a product search. For each instance of the decisiondata structure, manager system 110 can vary such threshold valueparameters and scoring value parameters within valid ranges.

Manager system 110 running machine learning process 117 can update thethreshold value and/or function value parameters of the differenceinstances of the decision data structure. Manager system 110 can monitorperformance of the difference instances and based on the performancemonitoring can update the parameters of the decision data structures sothat the parameters values over time converge toward values that are incommon with decision data structures that are determined to the be bestperforming decision data structures according to one or more criterion.

Manager system 110 can run preparation and maintenance process 111 topopulate and maintain data of data repository 112 for use by variousprocesses run by manager system 110 including e.g. product examiningprocess 115.

Manager system 110 can run NLP process 113 to process data forpreparation of records that are stored in data repository 112 and forother purposes. Manager system 110 can run a Natural Language Processing(NLP) process 113 for determining one or more NLP output parameter of amessage. NLP process 113 can include one or more of a topicclassification process that determines topics of messages, e.g. textbased messages and output one or more topic NLP output parameter, asentiment analysis process which determines sentiment parameter for amessage, e.g. polar sentiment NLP output parameters, “negative,”“positive,” and/or non-polar NLP output sentiment parameters, e.g.“anger,” “disgust,” “fear,” “joy,” and/or “sadness” or otherclassification process for output of one or more other NLP outputparameters e.g. one of more “social tendency” NLP output parameter orone or more “writing style” NLP output parameter.

By running of NLP process 113 manager system 110 can perform a number ofprocesses including one or more of (a) topic classification and outputof one or more topic NLP output parameter for a received message (b)sentiment classification and output of one or more sentiment NLP outputparameter for a received message or (c) other NLP classifications andoutput of one or more other NLP output parameter for the receivedmessage.

Topic analysis for topic classification and output of NLP outputparameters can include topic segmentation to identify several topicswithin a message. Topic analysis can apply a variety of technologiese.g. one or more of Hidden Markov model (HMM), artificial chains,passage similarities using word co-occurrence, topic modeling, orclustering. Sentiment analysis for sentiment classification and outputof one or more sentiment NLP parameter can determine the attitude of aspeaker or a writer with respect to some topic or the overall contextualpolarity of a document. The attitude may be the author's judgment orevaluation, affective state (the emotional state of the author whenwriting), or the intended emotional communication (emotional effect theauthor wishes to have on the reader). In one embodiment sentimentanalysis can classify the polarity of a given text at the document,sentence, or feature/aspect level whether the expressed opinion in adocument, a sentence or an entity feature/aspect is positive, negative,or neutral. Advanced sentiment classification can classify beyond apolarity of a given text. Advanced sentiment classification can classifyemotional states as sentiment classifications. Sentiment classificationscan include the classification of “anger,” “disgust,” “fear,” “joy,” and“sadness.”

Data repository 112 can store in product records area 2121 records ofproducts available for identification as a product of interest. For eachproduct in product records area 2121, there can be stored various datasuch as product name, brand name of a product, product identifier, e.g.product serial number, an image of a product, keywords associated to aproduct, specification of a product, a description of a product, and aprice of a product. System 100 can be configured so that anadministrator user can define data for inclusion in product records area2121 using an administrator user interface. Embodiments hereinfacilitate updating data of product records area 2121 by machinelearning. For example, decisions made by manager system 110 using topicclassifications derived by image analysis can result in topicclassifications being automatically appended to product record data.

Data repository 112 can store in decision data structures area 2122 oneor more decision data structure for use in providing decisions based onimage analysis derived topic classifications. In one embodiment, therecan be stored in decision data structures area 2122 a machine logicartificial intelligence (AI) decision mapping knowledgebase that mapsimage analysis based product dissimilarity scores to decisions, whereinthe decisions can involve including or not including a product in adetermined set of related products. In one embodiment, system 100 can beconfigured so that one or more decision data structure of decision datastructures area 2122 is automatically updated by machine learning.Products herein can include e.g., physical, tangible products orservices products, e.g. entertainment event services products orprofessional services products, e.g. accounting services, advertisingservices, legal services, and the like.

Embodiments herein recognize that services for returning relatedproducts related to an identified product of interest can returnerroneous information, based on search terms that have differentmeanings in different contexts. For example, a homograph is a term thathas different meanings in different contexts, e.g. “bass”, the type offish or “bass”, the type of musical instrument, or according to anotherexample “bow” referring to a type of decorative ribbon or “bow” theapparatus for projecting an arrow. Embodiments herein recognize thatnumerous and more subtle examples of terms having different meanings indifferent contexts can occur with respect to descriptions of products.For example, the term “collar” can refer to a type of jewelry apparel, aportion of a shirt, or an apparatus for attachment to neck area of adomestic pet such as a dog. The term “can” may refer, e.g. to anapparatus for storage of food items or alternatively to an apparatus fordisposal of refuse. The term “glasses” may refer, e.g. to drinkingglasses or eyewear. Embodiments herein employ image analysis todistinguish between contexts wherein the same term has differentmeanings across different contexts in commonly used product descriptionpresentments.

Embodiments herein further recognize that it can be advantageous tosimplify data entry tasks associated with registration of new productsinto a data repository storing product records. In fact, embodimentsherein recognize that product records are often defined and uploaded ona rush basis with limited content associated with the records.Embodiments herein recognize that product records provided withinlimited text based data e.g. on a rush basis have an increasedlikelihood of problems that are addressed by combinations that are setforth herein.

FIG. 2 depicts a flowchart illustrating coordination of processes thatcan be performed by manager system 110 of FIG. 1, in accordance with oneor more embodiments set forth herein.

At block 210, manager system 110 can run preparation and maintenanceprocess 111 to populate prepare and maintain various data of datarepository 112 including data of locations areas 2121-2122. Managersystem 110 can run preparation and maintenance process 111 iterativelyuntil process 111 is terminated at block 212.

At block 220, manager system 110 can run product examining process 115to determine related products related to one or more product ofinterest. For support of running of product examining process 115iteratively, manager system 110 can be running e.g. NLP process 113,product registering process 114, product examining process 115, imageanalysis process 116 and machine learning process 117 iteratively.Manager system 110 can run product examining process 115 until productexamining process 115 is terminated at block 222.

There is set forth herein in reference to FIG. 3, method 300 that can beperformed by manager system 110 according to one embodiment. Managersystem 110 at block 310 can perform obtaining from a user one or moredata query. Manager system 110 at block 320 can perform identifying aproduct of interest in response to the one or more data query. Managersystem 110 at block 330 can perform examining of a plurality of productrecords to determine a set of related products that are related to theproduct of interest, wherein the examining includes performing imageanalysis to extract one or more product topic classificationsrepresenting one or more product. Manager system 110 at block 340 canperform providing one or more output in response to the examining. Theset of related products that are related to the product of interestdetermined at block 330 can include one or more related product.

A specific example of method 300 is set forth in reference to theflowchart of FIG. 4 illustrating a specific example of method 300 fromthe perspective of manager system 110 interacting with data repository112 of manager system 110, administrator client computer device 125, andclient computer device 130A.

At block 1251, administrator client computer device 125 can send productregistration data to manager system 110 for receipt by manager system110 at block 1101. Product registration data can include data forregistration into product records area 2121 of data repository 112. Forsending of product registration data at block 1251, an administratoruser can use an administrator user interface such as administrator userinterface 500 depicted in FIG. 5. Administrator user interface 500 caninclude various data entry areas such as data entry area 502 forspecifying a product name, area 504 for specifying a product brand, area506 for specifying a product identifier, e.g. a product serial number,area 508 for specifying an image depicting a product, area 510 forspecifying keywords of a product, area 512 for entry of specificationdata of a product, area 514 for specifying a description of a product,and area 516 for specifying a price of a product. Referring to area 508of administrator user interface 500, area 508 in one embodiment canpermit an administrator user to select an image stored within a filedirectory accessible by an administrator user.

Administrator user interface 500 in one embodiment can be provided by awebpage form served by manager system 110. Administrator user interface500 can be an administrator user interface for display on a display ofadministrator client computer device 125.

In one embodiment, administrator client computer device 125 is anadministrator client computer device of an enterprise such as a retailenterprise that provides an online retail store. In another embodiment,system 100 can provide a retail store for access to products provided bymultiple different entities which can include, e.g. corporate entitiesand/or sole proprietorships (e.g. individuals posting content in anonline auction retail store environment). In one embodiment, system 100presents an online retail store environment provided by a crowdsourceonline retail store auction environment, in which different enterprises,which can include, e.g. corporate business enterprises and individualusers (e.g. sole proprietorships) can use administrator user interface500 to define product records having content such as product name,product brand, product identifier (e.g. product serial number), an imageof a product, product keywords, product specification, a productdescription, and/or a product price indexed under the respectiveheadings for data entry areas 502, 504, 506, 508, 510, 512, 514, and 516of FIG. 5.

In response to receiving product registration data, manager system 110at block 1102 can send received product registration data for receipt bydata repository 112 at block 1121 for storage of product registrationdata into product records area 2121 of data repository 112.Contemporaneously with storing of registration data manager system 110can activate NLP process 113 to return topic classifiers for textentered into one or more data entry areas 502, 504, 506, 510, 512, 514,and 516. Accordingly, product records of product records area caninclude product topic classifiers.

Each product for which data is stored in product records area 2121 caninclude the data entered using data entry areas 502, 504, 506, 508, 510,512, 514, and 516. Thus, there can stored in a record for each productan image as well as textual data. In some embodiment, text entered intokeywords area 510 of administrator user interface 500 can be subject toNLP processing by NLP process 113 to extract product topic classifiersbased on the keywords. In some embodiments, keywords that are enteredinto keywords area 510 can be processed by system 110 as product topicclassifiers, and extracted product classifiers extracted by processingperformed by manager system can be added to keyword data stored for aproduct of product records area 2121 of data repository 112 indexedunder the heading “keywords”.

Manager system 110 can perform receipt of configuration data provided byproduct registration data at block 1101 on an iterative basis. That is,administrator client computer device 125 can iteratively send productregistration data for receipt by manager system 110 at block 1101 on aniterative basis, e.g. as data for new products is entered byadministrator user, using administrator user interface 500. Further itwill be seen that in some instances a plurality of administrator userscan be using different instances of administrator user interface 500 forentry of product registration data for sending at block 1251. Thesending of product registration data at block 1251 and the receiving ofproduct registration data at block 1101 can be performed iterativelythroughout the lifetime of system 100, e.g. while instances offunctions, such as functions described herein with respect to blocks1103-1107 are being performed.

Client computer device 130A can perform sending and receiving atsend/receive block 1301 and manager system 110 can perform receiving andreturning prompts at receive/return prompt block 1103. Blocks 1301 and1103 refer according to one example, to a client server interactive webbrowsing session in which a client provided by client computer device130A is used by a user to search for data relating to various products,data of which can be stored in product records area 2121 of datarepository 112.

For example, a user can use a data entry area of user interface 1700 asshown in FIG. 7 to search for one or more product of interest. Userinterface 1700 can be displayed on a display of client computer device130A and can be a manually operated user interface. Data which may beentered by a user into a data entry area, such as data entry area 1702of user interface 1700 can include, e.g. a product serial number ordescriptive terms describing a product. Based on entered text entered adata entry area 1702 by a user using user interface 1700, manager system110 can identify one or more product of interest. On the identificationof one or more product of interest, manager system 110 can display inarea 1704 content specifying an identifies one or more product ofinterest. The content can include, e.g. an image depiction of theproduct of interest and/or a description of the product of interest. Asindicated by blocks 1301 and 1103, a user may enter into data entry area1702 one or more query and/or may interact with other active areas ofuser interface 1700 to specify one or more query prior to and based onsuch entered one or more query entered using user interface 1700.Manager system 110 can identify one or more product of interest and canresponsively display content respecting the identified one or moreproduct of interest in area 1704. In some use cases, manager system 110can return a single identified product of interest in response to asingle query, e.g. entered into data entry area 1702 of user interface1700. For example, the user can enter a product serial number and asingle product of interest can be identified based on the entered serialnumber. In other use cases, as indicated by block 1301 (specified to beiterative) and block 1103, a user can use different areas of userinterface 1700 to specify a succession of queries, e.g., in response todifferent prompts returned by manager system 110 prior to manager system110 finally identifying one or more product of interest and displayingcontent respecting the identified one or more product of interest inarea 1704.

Manager system 110 at block 1104 can determine whether or not a productof interest has been identified. If a product of interest has not beenidentified, manager system 110 at block 1104 can iteratively return toblock 1103 to iteratively send an additional one or more prompt toclient computer device 130A, prompting a user e.g. with menu optionsuntil sufficient data is received from client computer device 130Afacilitating the identification of one or more product of interest bymanager system 110. On determination that a product of interest has beenidentified at block 1104, manager system 110 can proceed to block 1105to perform examining of a plurality of product records of productrecords area 2121 to determine a set of related products that arerelated to the product of interest identified by manager system 110,wherein the examining includes performing image analysis, e.g. byactivating image analysis process 116 to extract one or more producttopic classifier representing one or more product. Performing examiningby manager system 110 at block 1105 can include multiple queries of datarepository 112 as indicated by query receive/response block 1122performed by data repository 112.

Function of manager system 110 performing examining at block 1105according to one embodiment is described with reference to process 5000set forth in reference to the flowchart of FIG. 7. At block 5002,manager system 110 can activate image analysis process 116 to performimage analysis of an identified product of interest image to extracttopic classifier(s) of the identified product of interest image. Atblock 5004, manager system 110 can perform identifying candidate relatedproducts of the identified product of interest and for each candidaterelated product of interest, manager system 110 can perform blocks 5006,5008, and 5010. At block 5006, manager system 110 can perform imageanalysis to extract classifier(s) of a candidate product image. At block5008, manager system 110 can perform comparing topic classifier(s) of acandidate related product(s) to topic classifier(s) of the identifiedproduct of interest. At block 5010, manager system 110 can performidentifying one or more output based on the comparing performed at block5008.

Referring to block 5002, manager system 110 can activate image analysisprocess 116 to return topic classifiers of an image specifying a productof interest. For example, referring to user interface 1700, an image ofa product of interest can be provided by the displayed image in area1704 specifying content of an identified product of interest. The imagedisplayed in area 1704 can be an image previously specified by anadministrator user using user interface 500 described in FIG. 5. In oneembodiment, an image analysis service can be provided by IBM Watson®Visual Recognition Services (IBM Watson is a registered trademark ofInternational Business Machines Corporation).

Illustrative content that can be returned by manager system 110 runningimage analyzing process 116 in respect to an identified product ofinterest image is described in reference to FIG. 8. The return contentcan include as indicated by content 1802, topic classifications for theanalyzed product image and associated confidence scores with eachclassification. In the example described in reference to FIG. 8, managersystem 110 can return for analyzed image 1801, the followingclassifications: necklace (confidence score 0.73), brachteole(confidence score 0.72), ivory color (confidence score 0.61), and bling(confidence score 0.55). Manager system 110 can further return markuplanguage content specifying the various classifications and confidencelevels. More specifically, content 1804 can include markup language textand syntax processable by process interfaces that specify the topicclassifications and associated confidence levels of content 1802.

Referring to block 5004, manager system 110 can perform identifyingcandidate related products for an identified product of interest usingtext based topic matching processes wherein text entered or menuselection specified by a user is used to determine topics that matchedto topics of records of product records area 2121. For example, managersystem 110 can match topics derived using text entered or specified by auser to topics derived by text of product records area 2121corresponding to text entered into one or more of area 502, area 504,area 506, area 510, area 512, area 514 of administrator user interface500 depicted in FIG. 5. Embodiments herein however recognize that suchtext derived topic matching can return erroneous results for reasons setforth herein, e.g. that a common descriptive term can have differentmeanings across different contexts and that the problem can beheightened when limited text data is entered e.g. on a rush basis intoan administrator user interface having characteristics as shown inreference to administrator user interface 500 of FIG. 5.

In the described example described with reference to user interface1700, illustrated in reference to FIG. 6, determined related productsdetermined to be related to a product of interest can be displayed inarea 1706 can include products that are potentially unrelated to theproduct of interest. In the described example based on the descriptiveterm “magnetic collar” which refers to an article of jewelry determinedrelated products displayed in area 1706 can include jewelry collars andcan also include pet collars. Embodiments herein provide featurizationsthat can suppress based on image analysis certain products from adetermined related products list defined by a determined set of relatedproducts. Referring further to the flowchart of FIG. 7, manager system110 with candidate related products for an identified product ofinterest identified at block 5004 can proceed to block 5006 to performimage analysis to extract topic classifier(s) of a candidate productimage.

At block 5006 manager system 110 for each identified candidate relatedproduct identified at block 5004 can perform image analysis on an imageof the candidate product to return various information such as one ormore topic classification of the candidate product image together withconfidence level information as well as markup language content. In thedescribed example described with reference to user interface 1700 setforth in FIG. 6, manager system 110 can identify the product describedas “striped dog collar” as a candidate product related to an identifiedproduct of interest.

FIG. 9 depicts illustrative content that can be returned by managersystem 110 by subjecting an image of such a candidate related product toimage analysis. Returned content can include classification content 1902specifying topic classifications and confidence levels associated withthe various topic classifications for an analyzed image such as imagecontent 1901 specifying the identified candidate related product.Classification content 1902 can include various classifications such asthe topic classifications as follows: ring shaped (confidence level0.81), canine (confidence level 0.75), metallic (confidence level 0.62),and belt (confidence level 0.53). Returned content returned as a resultof subjecting an image of a candidate related product to image analysiscan include markup language content 1904 which can include markuplanguage text and syntax specifying the various topic classificationsand confidence levels of image content 1901 to facilitate processing bya downstream one or more process interface.

Referring to comparing block 5008 manager system 110 can performcomparing topic classifiers of an identified product of interest totopic classifiers of identified candidate related products related totopic classifiers. For performing comparing at block 5008, managersystem 110 can use topic classifications extracted as a result of imageanalysis at blocks 5002 and 5006 and/or manager system 110 can usepreexisting topic classifiers from product records area 2121, whichtopic classifiers can be specified by a user, e.g. by specifyingkeywords in keyword area 510 of user interface 500 as shown in FIG. 5and/or as may be determined by manager system 110 by subjecting textentered into one or more of area 502, 504, 506, 510, 512, 514 toprocessing by NLP process 113 to determine topic classifications of suchentered text. Whether or not preexisting topic classifiers are used atblock 5008, manager system 110 in performing comparing at block 5008 canperform comparing in dependence on topic classifications returned as aresult of image analysis at block 5002 and/or block 5006. In oneembodiment, block 5002 can be deleted and manager system 110 can performcomparing at block 5008 by comparing preexisting topic classifiers ofproduct records area 2121 to topic classifiers including topicclassifiers of a candidate related product including topic classifiersreturned by image analysis respecting a candidate product image at block5006. In one embodiment, block 5006 can be deleted and comparing atblock 5008 can include comparing of product of interest topicclassifiers including topic classifiers returned as a result of imageanalysis at block 5002 to topic classifiers of identified candidateproducts of interest, wherein the topic classifiers of the candidateproducts of interest are preexisting topic classifiers of productrecords area 2121.

In one embodiment, comparing by manager system 110 at block 5008 caninclude manager system 110 performing dissimilarity scoring processing.Manager system 110 performing comparing at block 5008 in one embodimentcan include manager system 110 using Eq. 1 as follows.DS=F ₁ W ₁ −F ₂ W ₂  (Eq. 1)Where DS is a dissimilarity score between an identified product ofinterest and an identified candidate related product, where F₁ is atopic match failure factor, F₂ is a topic match success factor, andwhere W₁ and W₂ are weights, respectively, associated with factors F₁and F₂.

Manager system 110 for comparing an identified product of interest to anidentified candidate related product can compare topic classificationsfor the identified product of interest to topic classifications for theidentified candidate related product. Where manager system 110 finds nomatch for a topic classification of an identified product of interest,manager system can increment by one a topic match failure accumulator.Where manager system 110 identifies a match between a topicclassification of an identified product of interest and a candidaterelated product, manager system 110 can increment by one a topic matchsuccess accumulator. In one embodiment, the factor F₁ can be provided asa percentage of attempted matches resulting in topic match failures andF₂ can be provided as a percentage of attempted topic matches resultingin topic matching successes. In one embodiment, these values can bescaled according to confidence levels associated with various topicclassifications. For example, matching between topics having respective0.80 and 0.70 confidence levels can increment a topic matchingaccumulator by 0.75 (the average of 0.80 and 0.75) rather than 1.0.Based on Eq. 1, it can be seen that a relatively high dissimilarityscore (DS) can be provided when there are several topic classificationmatching failures and few topic classification matching successes andthat a relatively low dissimilarity score (DS) can be provided whenthere is a high percentage of topic matching attempts that are topicmatching successes.

At block 5010, manager system 110 can perform identifying of one or moreoutput based on comparing performed at block 5008. The identifying oneor more output at block 5010 can include, e.g. identifying a determinedset of one or more related products for presentment of a notification toa user and/or determined menu options, e.g. for presentment to anadministrator user and/or to a user performing a search for return of anidentified product of interest. Manager system 110 performingidentifying one or more output at block 5010 can include manager system110 using one or more decision data structure of decision datastructures area 2122.

Referring to blocks 5008 and 5010, manager system 110 can performidentifying one or more output based on a comparing using a decisiondata structure of decision data structures area 2122 in accordance withthe decision data structure set forth in reference to Table 1, whichmaps various dissimilarity scores (DS) to various decisions, e.g. A, B,C, D, E and F that specify various actions by manager system 110.

TABLE 1 Dissimilarity Score (DS) Decision DS ≥ T₁ A: Auto-suppresscandidate related product from a set of related products for display onuser interface 1700; B: Auto-update all product topic classificationlists for all products (product of interest or candidate relatedproducts) subject to image analysis; C: Notification to administratoruser specifying suppression and update of topic classification lists.T₁ > DS ≥ T₂ D: Present menu option to user to suppress candidaterelated product; E: Present notification and menu option toadministrator user to add image analysis extracted product topicclassifiers to product topic classifier lists (product of interest orcandidate related product) subject to image analysis. DS < T₂ Nosuppression of candidate related product from set of determined relatedproducts; no update of product topic classifier lists to include imageanalysis determined topic classifiers; F: Present administrator usernotification and menu option to add image analysis determined producttopic classifiers to product topic classifier lists in an alternativeembodiment.

As set forth in reference to the decision data structure of Table 1,manager system 110 can return various decisions that are differentiatedbased on a value of a dissimilarity score (DS). For dissimilarity scoresabove or equal to the threshold T₁, indicating a high degree ofdissimilarity manager system 110 in one embodiment can return thedecisions A, B, and C, wherein A is the decision to auto-suppress acandidate related product from a set of determined related products andauto-suppress candidate related products from a set of related productsfor display on user interface 1700 and B is the decision to auto-updateall product topic classification lists for all products (product ofinterest or candidate related products) subject to image analysis atblocks 5002 and/or 5006 and where in the decision C is the decision tosend a notification to an administrator user, e.g. using administratoruser interface 500 specifying the decision to suppress a candidaterelated product from a set of determined related products for displayand the decision to update product topic classifications lists.

Further in accordance with the decision data structure of Table 1,manager system 110 in the case that a dissimilarity score (DS) between acompared product of interest and a candidate related product ismoderate, e.g. as indicated by being greater than a second threshold T₂but less than the threshold T₁ manager system 110 can return decisions Dand E, wherein decision D is the decision to present a menu option to auser searching for a product of interest, e.g. using user interface 1700to suppress a candidate related product and wherein E is the decision topresent a menu option to an administrator user to add image analysisextracted product topic classifiers to product topic classifier lists(product of interest or candidate related product) subject to imageanalysis.

With further reference to the decision data structure of Table 1,manager system 110 in the case that a product of interest is similar toa candidate related product, e.g. where a dissimilarity score (DS) isless than the threshold T₂, manager system 110 can determine that thecompared candidate product of interest is to be included in a determinedproduct of interest and can perform no suppression of a candidaterelated product from a set of determined related products and canperform no updating of product topic classifier lists to include imageanalysis topic classifiers. In an alternative embodiment, manager system110 based on a dissimilarity score (DS) being less than a threshold T₂,can present an administrator user, e.g. using administrator userinterface 500 a menu option to add image analysis determined producttopic classifiers to product topic classifier lists. Referring to userinterface 1700 (FIG. 6), manager system 110 based on decision A beingreturned can suppress altogether the displayed content from area 1712.Referring to administrator user interface 500 based on decision C beingreturned, manager system 110 can display in area 522 of administratoruser interface 500 the information that based on a dissimilarity score(DS) exceeding the threshold T₁ the image analysis extracted producttopic classifiers of classification content 1802 (FIG. 8) andclassification content 1902 (FIG. 9) have been added to topicclassification lists of product records area 2121 of data repository112.

Based on the decision D being returned manager system 110 can present amenu option prompt in area 1714 of user interface 1700 allowing a usersearching for a product of interest to specify that a currentlyrecommended product indicated in area 1712 be suppressed from adetermined related product list. In one embodiment, manager system 110clicking on area 1714 to suppress the recommended product of area 1712can automatically update product topic classification lists of theproduct of interest and the candidate related product indicated in area1712 to include image analysis extracted product topic classificationextracted at blocks 5002 and/or 5006. Based on decision E beingreturned, manager system 110 referring to administrator user interface500, can present in area 526 and/or area 528 menu options that can beexercised by an administrator user to add image analysis determinedproduct topic classifications determined at block 5002 and/or block 5006to current product topic classification lists for a product of interestand/or a candidate related product subject to comparing at block 5008 inproduct records area 2121. Manager system 110 can also display menuoption area 526 and/or menu option area 528 in response to decision Fbeing returned using a decision data structure according to the decisiondata structure set forth in Table 1.

According to one embodiment, decisions returned using a decision datastructure as set forth in Table 1 can include additional features sothat manager system 110 returns a minimal number of related products.Thus, in one implementation manager system 110 can be restricted fromenforcing decision A to auto-suppress candidate related products from aset of related products for display on user interface 1700 on thecondition the product is needed to satisfy a minimal number of relatedproduct return criterion. In one embodiment, manager system 110 canprovide thresholds T₁ and T₂ to be in dependence on a minimal number ofrelated product return criterion to assure return of the relatedproducts in accordance with the minimal number of related product returncriterion.

Referring again to the flowchart of FIG. 4, manager system 110 oncompletion of examining block 1105 can proceed to block 1106 to provideone or more output. The one or more output can be in accordance with theidentified outputs described with reference to block 5010 (FIG. 7) andcan include one or more output to provide the actions described inreference to Table 1 in accordance with one or more of the decisions A,B, C, D, E, and/or F set forth in reference to the decision datastructure of Table 1. In the case the one or more output includes anotification to an administrator user the notification can be receivedby an administrator client computer device 125 at block 1252. In thecase the one or more output includes a notification to a user thenotification can be received by a client computer device 130A at block1302.

The exemplary decision data structure as set forth in Table 1 hasspecific threshold parameter values and is in dependence on scoringvalue including weight parameters of Eq. 1. In one embodiment, managersystem 110 can run machine learning process 117 to iteratively updatethe decision data structure such as the decision data structure of Table1 by machine learning. In one embodiment, manager system 110 can monitorperformance of instances of a decision data structure. Performance canbe monitored based on one or more criterion, e.g. number of generatedadditional clicks on displayed content of determined related products,purchases of determined related products, and the like. In oneembodiment, a click of displayed content featuring a candidate relatedproduct suppressed with a first instance of decision structure activeand not suppressed with a second instance of the decision data structureactive can be used to negatively score performance of the firstinstance. In one embodiment, any click of displayed content featuring acandidate related product can be used to positively score the instanceof the decision data structure driving the presented display of therelated products.

Manager system 110 running machine learning process 117 in oneembodiment can iteratively examine performance of manager system 110across a distribution of instances, wherein each instance employsdifferently configured decision data structure (e.g. havingdifferentiated parameter values) for driving a related productdetermination decision. Manager system 110 can be running a plurality ofinstances of blocks 5008 and 5010 for a plurality of user searchesconcurrently or successively. For each instance, manager system 110 canchange parameter values shown in Eq. 1 and Table 1 within valid rangesto facilitate searching of optimum parameter values and manager system110 by running machine learning process 117 can update the parametervalues over the time so that the parameter values over time (as moresample data is accrued) converge toward values that are optimallyperforming parameter values. Based on a result of monitoring ofdifferent instances over time, manager system 110 running machinelearning process 117 can bias the variety of the decision datastructures employed, so that the decision data structures of decisiondata structures area 2122 assume the characteristics of the mostsuccessfully performing iterations.

Another feature that can be incorporated into system 100 is set forthwith respect to administrator user interface 500 illustrated in FIG. 5.In one embodiment manager system 110 can provide user interface 500 toinclude an accelerator function that accelerates the population ofkeywords into area 510. Keywords entered into area 510 (normally by anadministrator user) can be used as product topic classifiers and/or orprocessed by activation of NLP process 113 for extraction of producttopic classifiers. User interface 500 can be provided so that foracceleration of text based keywords into keywords area 510, anadministrator user can click on accelerator control 532 to activateimage analysis process 116. Activation of image analysis process 116 cancause a one or more image selected using area 508 (which one or moreimage can be currently displayed on administrator user interface 500) tobe subjected to image analyzes for extraction of content as depicted inFIGS. 8 and 9 and including one or more product topic classifiers. Thetext specifying the one or more extracted topic classifier can beauto-populated into area 510 for review by the administrator user who isusing administrator user interface 500 to define product registrationdata. The administrator user can decide to remove the auto-populated oneor more keyword by using an editing function of the administrator userinterface. The administrator user can decide to not remove theauto-populated one or more keyword by not using an editing function ofthe administrator user interface. If the auto-populated one or morekeyword is not removed, it will become part of a product data recordstored in product records area 2121 on being sent at block 1251. Thefeature can facilitate rapid growth of a corpus of records into productrecords area 2121 of data repository. For example, for rapid definitionof data records, an administrator user can select N images of a currentproduct being subject to record definition using area 508. Theadministrator user can then use control 532 to activate image analysisprocess 116 and responsive to such activation several keyword termsspecifying extracted product topic classifications extracted by theimage analysis can be auto-populated into keywords area 510, which canauto-populated keywords (like all keywords of area 510) can be subjectto editing by an administrator user using a manual editing function ofadministrator user interface 500.

Certain embodiments herein may offer various technical computingadvantages involving computing advantages to address problems arising inthe realm of computer networks. Embodiments herein can employ imageanalysis to improve accuracy and reliability of decision data structuresfor return of decisions. Embodiments herein involve computing advantagesto address problems arising in the realm of computer networks such asproblems involving user interfacing performed by computer networks inapplications wherein inaccurate information or misalignment with auser's attention state can yield user disengagement and wasted andunnecessary computing resource expenditures. Embodiments hereinrecognize that interactions between a computer network and a user of thecomputer network are fundamental to the operation of the computernetwork. For example if information presented to a user is inaccurate ormisaligned to a user's state of attention, the user can disengage fromthe network leading to a range of problems. Computing resources will beallocated to providing functions not utilized to deliriously effectefficiencies of other services provided. Computing resources may beunnecessary allocated to facilitate an unnecessary session terminationprocess and additional computing resources to facilitate an unnecessaryre-login process and an unnecessary re-authentication process.Embodiments herein recognize that a user interfacing with a computernetwork can be expected to disengage of presented with information thatis inaccurate or misaligned to a current state of attention of a user,e.g., if a computers network's response to a user's initiated query ismisaligned to a target of the user. Various decision data structures canbe used to drive artificial intelligence (AI) decision making, such asdecision data structures that cognitively maps dissimilarity scores todecisions that determine one or more output. Embodiments herein canfeature artificial intelligence (AI) platforms that replicate aspects ofhuman cognitive functioning e.g. by contemporaneous processing of textbased and spatial image based inputs. Embodiments herein can includeartificial intelligence (AI) processing platforms featuring improvedprocesses to transform unstructured data into structured form permittingcomputer based analytics and predictive decision making. Embodimentsherein can include particular arrangements for both collecting rich datainto a data repository and additional particular arrangements forupdating such data and for use of that data to drive artificialintelligence decision making. Decision data structures as set forthherein can be updated by machine learning so that accuracy andreliability is iteratively improved over time without resource consumingrules intensive processing. Machine learning processes can be performedfor increased accuracy and for reduction of reliance on rules basedcriteria and thus reduced computational overhead. For enhancement ofcomputational accuracies, embodiments can feature computationalplatforms existing only in the realm of computer networks such asartificial intelligence platforms, and machine learning platforms.Embodiments herein can employ data structuring processes, e.g. employingNatural Language Processing (NLP) and decision data structure processingfor transforming unstructured data into a form optimized forcomputerized processing.

FIGS. 10-12 depict various aspects of computing, including a computersystem and cloud computing, in accordance with one or more aspects setforth herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 10, a schematic of an example of a computing nodeis shown. Computing node 10 is only one example of a computing nodesuitable for use as a cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein. Regardless, computingnode 10 is capable of being implemented and/or performing any of thefunctionality set forth hereinabove. Computing node 10 can beimplemented as a cloud computing node in a cloud computing environment,or can be implemented as a computing node in a computing environmentother than a cloud computing environment.

In computing node 10 there is a computer system 12, which is operationalwith numerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system 12 include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 12 may be described in the general context of computersystem-executable instructions, such as program processes, beingexecuted by a computer system. Generally, program processes may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program processes may belocated in both local and remote computer system storage media includingmemory storage devices.

As shown in FIG. 10, computer system 12 in computing node 10 is shown inthe form of a general-purpose computing device. The components ofcomputer system 12 may include, but are not limited to, one or moreprocessor 16, a system memory 28, and a bus 18 that couples varioussystem components including system memory 28 to processor 16. In oneembodiment, computing node 10 is a computing node of a non-cloudcomputing environment. In one embodiment, computing node 10 is acomputing node of a cloud computing environment as set forth herein inconnection with FIGS. 11-12.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 12 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 12, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program processes that are configured to carry out thefunctions of embodiments of the invention.

One or more program 40, having a set (at least one) of program processes42, may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram processes, and program data. One or more program 40 includingprogram processes 42 can generally carry out the functions set forthherein. In one embodiment, manager system 110 can include one or morecomputing node 10 and can include one or more program 40 for performingfunctions described with reference to method 200 of FIG. 2 and functionsdescribed with reference to method 300 of FIG. 3 and functions describedwith reference to manager system 110 as set forth in the flowchart ofFIG. 4. In one embodiment, one or more of client computer devices130A-130Z can include one or more computing node 10 and can include oneor more program 40 for performing functions described with reference toone or more user computer device 130A-130Z as set forth in the flowchartof FIG. 4. In one embodiment, one or more administrator client computerdevice 125 can include one or more computing node 10 and can include oneor more program 40 for performing functions described with reference toadministrator client computer device 125 as set forth in the flowchartof FIG. 4. In one embodiment, the computing node based systems anddevices depicted in FIG. 1 can include one or more program forperforming function described with reference to such computing nodebased systems and devices.

Computer system 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computer system12; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 12 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces22. Still yet, computer system 12 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter 20 communicates with the othercomponents of computer system 12 via bus 18. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 12. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc. In addition to or in place of havingexternal devices 14 and display 24, which can be configured to provideuser interface functionality, computing node 10 in one embodiment caninclude display 25 connected to bus 18. In one embodiment, display 25can be configured as a touch screen display and can be configured toprovide user interface functionality, e.g. can facilitate virtualkeyboard functionality and input of total data. Computer system 12 inone embodiment can also include one or more sensor device 27 connectedto bus 18. One or more sensor device 27 can alternatively be connectedthrough I/O interface(s) 22. One or more sensor device 27 can include aGlobal Positioning Sensor (GPS) device in one embodiment and can beconfigured to provide a location of computing node 10. In oneembodiment, one or more sensor device 27 can alternatively or inaddition include, e.g., one or more of a camera, a gyroscope, atemperature sensor, a humidity sensor, a pulse sensor, a blood pressure(bp) sensor or an audio input device. Computer system 12 can include oneor more network adapter 20. In FIG. 11 computing node 10 is described asbeing implemented in a cloud computing environment and accordingly isreferred to as a cloud computing node in the context of FIG. 11.

Referring now to FIG. 11, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 11 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 12, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 11) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 12 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and processing components 96 for performingpredictions of candidate geofence performance and related processes setforth herein. The processing components 96 can be implemented with useof one or more program 40 described in FIG. 10.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprise” (and any form ofcomprise, such as “comprises” and “comprising”), “have” (and any form ofhave, such as “has” and “having”), “include” (and any form of include,such as “includes” and “including”), and “contain” (and any form ofcontain, such as “contains” and “containing”) are open-ended linkingverbs. As a result, a method or device that “comprises,” “has,”“includes,” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises,” “has,” “includes,” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Forms of the term“based on” herein encompass relationships where an element is partiallybased on as well as relationships where an element is entirely based on.Methods, products and systems described as having a certain number ofelements can be practiced with less than or greater than the certainnumber of elements. Furthermore, a device or structure that isconfigured in a certain way is configured in at least that way, but mayalso be configured in ways that are not listed.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description set forth herein has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of one or more aspects set forth herein and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects as described herein for variousembodiments with various modifications as are suited to the particularuse contemplated.

What is claimed is:
 1. A method comprising: obtaining from a user a dataquery, wherein the data query is a text based data query entered by theuser; identifying a product of interest in response to the text baseddata query obtained from the user; examining a plurality of productrecords to determine a set of related products that are related to theproduct of interest, wherein the examining includes performing imageanalysis to extract one or more product topic classifier from productimage data representing one or more product, wherein the performingimage analysis includes subjecting a product image of the product ofinterest to image analysis to extract one or more product topicclassifier of the product of interest; and providing one or more outputin response to the examining, wherein the method includes identifying acandidate related product in response to the text based data queryobtained from the user, wherein the performing image analysis includessubjecting a product image of the product of interest to image analysisto extract a first list of product topic classifiers associated theproduct of interest, and wherein the performing image analysis includessubjecting a product image of the candidate related product to imageanalysis to extract a second list of product topic classifiersassociated to the candidate related product, wherein the method includesperforming dissimilarity processing to determine a dissimilarity betweenthe product of interest and the candidate related product, wherein theperforming the dissimilarity processing includes discovering producttopic classifiers of the first list that are not matched to a producttopic classifier of the second list, and ascertaining product topicclassifiers of the first list that are matched to a product topicclassifier of the second list, and providing a dissimilarity score usingthe discovering and the ascertaining, wherein the examining a pluralityof product records to determine a set of related products that arerelated to the product of interest comprises including the candidaterelated product in the set of related products based on thedissimilarity score being less than a threshold, and wherein theproviding one or more output in response to the examining includesdisplaying the candidate related product to the user on a user interfaceof the user based on the dissimilarity score being less than thethreshold.
 2. The method of claim of 1, wherein the performing imageanalysis includes subjecting a product image of a second candidaterelated product for the product of interest to image analysis to extractone or more product topic classifier of the second candidate relatedproduct.
 3. The method of claim of 1, wherein the performing imageanalysis includes subjecting a product image of the product of interestto image analysis to extract one or more first product topic classifierof the product of interest, and wherein the performing image analysisincludes subjecting a product image of a second candidate relatedproduct for the product of interest to image analysis to extract one ormore second product topic classifier of the second candidate relatedproduct, and wherein the examining includes comparing first dataincluding the one or more first topic classifier to second dataincluding the one or more second topic classifier, and the wherein theproviding the one or more output in response to the examining includesexcluding the second candidate related product from a determined set ofrelated products based on the comparing indicating that a dissimilarityof the second candidate related product to the product of interestexceeds a threshold.
 4. The method of claim of 1, wherein theidentifying the product of interest in response to the one or more dataquery includes processing product records stored in a data repository,wherein the performing image analysis includes subjecting a productimage of the product of interest to image analysis to extract one ormore product topic classifier of the product of interest, and whereinthe one or more output include updating the data repository so that aproduct of the data repository includes at least one of the one or moreproduct topic classifier of the product of interest.
 5. The method ofclaim 1, wherein the performing image analysis includes subjecting aproduct image of the product of interest to image analysis to extractone or more first product topic classifier of the product of interest,and wherein the performing image analysis includes subjecting a productimage of a second candidate related product for the product of interestto image analysis to extract one or more second product topic classifierof a candidate related product, and wherein the examining includesproducing a dissimilarity score using first data including the one ormore first topic classifier and second data including the one or moresecond topic classifier, and wherein the providing the one or moreoutput in response to the examining includes excluding the secondcandidate related product from a determined set of related productsbased on the dissimilarity score indicating that a dissimilarity of thesecond candidate related product to the product of interest exceeds athreshold.
 6. The method of claim 1, wherein the providing the one ormore output in response to the examining includes using a dissimilarityscore to decision mapping knowledgebase that maps product of interest tocandidate related product dissimilarity scores to decisions associatedto the product of interest to candidate related product dissimilarityscores, wherein the providing one or more output includes providing oneor more output to perform a first action in response to thedissimilarity score having a first value, and wherein the providing oneor more output includes providing one or more output to perform a secondaction in response to a second dissimilarity score having a secondvalue.
 7. The method of claim 1, wherein the providing the one or moreoutput in response to the examining includes using a dissimilarity scoreto decision mapping knowledgebase that maps product of interest tocandidate related product dissimilarity scores to decisions associatedto the product of interest to candidate related product dissimilarityscores, wherein the providing one or more output includes providing oneor more output to perform a first action in response to thedissimilarity score having a first value, and wherein the providing oneor more output includes providing one or more output to perform a secondaction in response to a second dissimilarity score having a secondvalue, wherein the first action is displaying data of the candidaterelated product in a display area of a display featuring relatedproducts, wherein the second action is suppressing data of the candidaterelated product in a display area of the display featuring relatedproducts related to the product of interest.
 8. The method of claim 1,wherein the providing the one or more output in response to theexamining includes using a dissimilarity score to decision mappingknowledgebase that maps product of interest to candidate related productdissimilarity scores to decisions associated to the product of interestto candidate related product dissimilarity scores, wherein the providingone or more output includes providing one or more output to perform afirst action in response to the dissimilarity score having a firstvalue, and wherein the providing one or more output includes providingone or more output to perform a second action in response to a seconddissimilarity score having a second value, wherein the first action ispresenting a user performing a product of interest search a menu optionto suppress displaying data of the candidate related product in adisplay area of a display featuring related products, wherein the secondaction is presenting an administrator user who has added record data ofthe candidate related product into a data repository a menu option toadd a product topic classifier extracted by image analysis of an imageof the candidate related product into a data record for the candidaterelated product within the data repository.
 9. The method of claim 1,wherein the providing the one or more output in response to theexamining includes using a dissimilarity score to decision mappingknowledgebase that maps product of interest to candidate related productdissimilarity scores to decisions associated to the product of interestto candidate related product dissimilarity scores, wherein the providingone or more output includes providing one or more output to perform afirst action in response to the dissimilarity score having a firstvalue, wherein the method includes monitoring performance of thedissimilarity score to decision mapping knowledgebase and updatingparameter values of the dissimilarity score to decision mappingknowledgebase by machine learning based on the monitoring, wherein themonitoring includes monitoring of actions with respect to webpagecontent produced using the dissimilarity score to decision mappingknowledgebase.
 10. The method of claim 1, wherein the method includesproviding the plurality of product records using a displayedadministrator user interface that has an image selection area tofacilitate selection of one or more image of a product by anadministrator user, and also includes a keyword area configured tofacilitate entry of keyword text by the administrator user, wherein theadministrator user interface is configured to permit the administratoruser to activate an image analysis process wherein a selected one ormore image of a product by the administrator user selected using theimage selection area is subject to image analysis to extract one or moreproduct classifiers of a product represented in the selected one or moreimage, and wherein responsively to activation of the image analysisprocess by the administrator user auto-populated text specifying the oneor more extracted product classifiers is auto-populated into the keywordarea in a form that permits editing of the auto-populated text by theadministrator user.
 11. A computer program product comprising: acomputer readable storage medium readable by one or more processingcircuit and storing instructions for execution by one or more processorfor performing a method comprising: obtaining from a user a data query,wherein the data query is a text based data query entered by the user;identifying a product of interest in response to the text based dataquery obtained from the user; examining a plurality of product recordsto determine a set of related products that are related to the productof interest, wherein the examining includes performing image analysis toextract one or more product topic classifier from product image datarepresenting one or more product, wherein the performing image analysisincludes subjecting a product image of the product of interest to imageanalysis to extract one or more product topic classifier of the productof interest; and providing one or more output in response to theexamining, wherein the method includes identifying a candidate relatedproduct in response to the text based data query obtained from the user,wherein the performing image analysis includes subjecting a productimage of the product of interest to image analysis to extract a firstlist of product topic classifiers associated the product of interest,and wherein the performing image analysis includes subjecting a productimage of the candidate related product to image analysis to extract asecond list of product topic classifiers associated to the candidaterelated product, wherein the method includes performing dissimilarityprocessing to determine a dissimilarity between the product of interestand the candidate related product, wherein the performing thedissimilarity processing includes discovering product topic classifiersof the first list that are not matched to a product topic classifier ofthe second list, and ascertaining product topic classifiers of the firstlist that are matched to a product topic classifier of the second list,and providing a dissimilarity score using the discovering and theascertaining, wherein the examining a plurality of product records todetermine a set of related products that are related to the product ofinterest comprises including the candidate related product in the set ofrelated products based on the dissimilarity score being less than athreshold, and wherein the providing one or more output in response tothe examining includes displaying the candidate related product to theuser on a user interface of the user based on the dissimilarity scorebeing less than the threshold.
 12. The computer program product of claim11, wherein the performing image analysis includes subjecting a productimage of a second candidate related product for the product of interestto image analysis to extract one or more product topic classifier of thesecond candidate related product.
 13. The computer program product ofclaim 11, wherein the performing image analysis includes subjecting aproduct image of the product of interest to image analysis to extractone or more first product topic classifier of the product of interest,and wherein the performing image analysis includes subjecting a productimage of a second candidate related product for the product of interestto image analysis to extract one or more second product topic classifierof the second candidate related product, and wherein the examiningincludes comparing first data including the one or more first topicclassifier to second data including the one or more second topicclassifier, and wherein the providing the one or more output in responseto the examining includes excluding the candidate related product from adetermined set of related products based on the comparing indicatingthat a dissimilarity of the candidate related product to the product ofinterest exceeds a threshold.
 14. The computer program product of claim11, wherein the plurality of records are stored in a data repository andwherein the providing the one or more output in response to theexamining includes using a dissimilarity score to decision mappingknowledgebase that maps product of interest to candidate related productdissimilarity scores to decisions associated to the product of interestto candidate related product dissimilarity scores, wherein the providingone or more output includes providing one or more output to perform afirst action in response to the dissimilarity score having a firstvalue, and wherein the providing one or more output includes providingone or more output to perform a second action in response to a seconddissimilarity score having a second value, wherein the first action ispresenting a user performing a product of interest search a menu optionto suppress displaying data of the candidate related product in adisplay area of a display featuring related products, wherein the secondaction is presenting an administrator user who has added record data ofthe candidate related product into the data repository a menu option toadd a product topic classifier extracted by image analysis of an imageof the candidate related product into a data record for the candidaterelated product within the data repository, wherein the method includesproviding the plurality of product records using a displayedadministrator user interface that has an image selection area tofacilitate selection of one or more image of a product by theadministrator user, and also includes a keyword area configured tofacilitate entry of keyword text by the administrator user, wherein theadministrator user interface is configured to permit the administratoruser to activate an image analysis process wherein a selected one ormore image of a product by the administrator user selected using theimage selection area is subject to image analysis to extract one or moreproduct classifiers of a product represented in the selected one or moreimage, and wherein responsively to activation of the image analysisprocess by the administrator user auto-populated text specifying the oneor more extracted product classifiers is auto-populated into the keywordarea in a form that permits editing of the auto-populated text by theadministrator user.
 15. The computer program product of claim 11,wherein the plurality of records are stored in a data repository andwherein the providing the one or more output in response to theexamining includes using a dissimilarity score to decision mappingknowledgebase that maps product of interest to candidate related productdissimilarity scores to decisions associated to the product of interestto candidate related product dissimilarity scores, wherein the providingone or more output includes providing the one or more output to performa first action in response to the dissimilarity score having a firstvalue, and wherein the providing the one or more output includesproviding one or more output to perform a second action in response to asecond dissimilarity score having a second value, wherein the firstaction is presenting a user performing a product of interest search amenu option to suppress displaying data of the candidate related productin a display area of a display featuring related products, wherein thesecond action is presenting an administrator user who has added recorddata of the candidate related product into the data repository a menuoption to add a product topic classifier extracted by image analysis ofan image of the candidate related product into a data record for thecandidate related product of within the data repository, wherein themethod includes providing the plurality of product records using adisplayed administrator user interface that has an image selection areato facilitate selection of one or more image of a product by theadministrator user, and also includes a keyword area configured tofacilitate entry of keyword text by the administrator user, wherein theadministrator user interface is configured to permit the administratoruser to activate an image analysis process wherein a selected one ormore image of a product by the administrator user selected using theimage selection area is subject to image analysis to extract one or moreproduct classifiers of a product represented in the selected one or moreimage, and wherein responsively to activation of the image analysisprocess by the administrator user auto-populated text specifying the oneor more extracted product classifiers is auto-populated into the keywordarea in a form that permits editing of the auto-populated text by theadministrator user, wherein the providing the one or more output inresponse to the examining includes using a dissimilarity score todecision mapping knowledgebase that maps product of interest tocandidate related product dissimilarity scores to decisions associatedto the product of interest to candidate related product dissimilarityscores, wherein the providing the one or more output includes providingthe one or more output to perform a first action in response to thedissimilarity score having a first value, wherein the method includesmonitoring performance of the dissimilarity score to decision mappingknowledgebase and updating parameter values of the dissimilarity scoreto decision mapping knowledgebase by machine learning based on themonitoring, wherein the monitoring includes monitoring of actions withrespect to webpage content produced using the dissimilarity score todecision mapping knowledgebase.
 16. A system comprising: a memory; atleast one processor in communication with the memory; and programinstructions executable by one or more processor via the memory toperform a method comprising: obtaining from user a data query, whereinthe data query is a text based data query entered by the user;identifying a product of interest in response to the text based dataquery obtained from the user; examining a plurality of product recordsto determine a set of related products that are related to the productof interest, wherein the examining includes performing image analysis toextract one or more product topic classifier from product image datarepresenting one or more product, wherein the performing image analysisincludes subjecting a product image of the product of interest to imageanalysis to extract one or more product topic classifier of the productof interest; and providing one or more output in response to theexamining, wherein the method includes identifying a candidate relatedproduct in response to the text based data query obtained from the user,wherein the performing image analysis includes subjecting a productimage of the product of interest to image analysis to extract a firstlist of product topic classifiers associated the product of interest,and wherein the performing image analysis includes subjecting a productimage of the candidate related product to image analysis to extract asecond list of product topic classifiers associated to the candidaterelated product, wherein the method includes performing dissimilarityprocessing to determine a dissimilarity between the product of interestand the candidate related product, wherein the performing thedissimilarity processing includes discovering product topic classifiersof the first list that are not matched to a product topic classifier ofthe second list, and ascertaining product topic classifiers of the firstlist that are matched to a product topic classifier of the second list,and providing a dissimilarity score using the discovering and theascertaining, wherein the examining a plurality of product records todetermine a set of related products that are related to the product ofinterest comprises including the candidate related product in the set ofrelated products based on the dissimilarity score being less than athreshold, and wherein the providing one or more output in response tothe examining includes displaying the candidate related product to theuser on a user interface of the user based on the dissimilarity scorebeing less than the threshold.
 17. The system of claim of 16, whereinthe method includes identifying a second candidate related product inresponse to the text based data query obtained from the user, whereinthe performing image analysis includes subjecting a product image of theproduct of interest to image analysis to extract one or more firstproduct topic classifier of the product of interest, and wherein theperforming image analysis includes subjecting a product image of thesecond candidate related product for the product of interest to imageanalysis to extract one or more second product topic classifier of thesecond candidate related product, and wherein the examining includescomparing first data including the one or more first topic classifier tosecond data including the one or more second topic classifier, andwherein the providing the one or more output in response to theexamining includes excluding the candidate related product from adetermined set of related products based on the comparing indicatingthat a dissimilarity of the candidate related product to the product ofinterest exceeds a threshold.